Determining a final exposure setting automatically for a sold state camera without a separate light metering circuit

ABSTRACT

An embodiment of the invention is a method of generating a final exposure setting, including, (a) selecting one of a number of predetermined exposure settings as a current exposure setting for a solid state camera having a camera imager, (b) generating a captured scene by the camera imager using the current exposure setting, (c) selecting according to an automated search methodology another one of the exposure settings to be the current setting in response to the captured scene being underexposed or overexposed, and, (d) repeating (b) and (c) until the captured scene is neither underexposed or overexposed.

This Application is a Continuation of Ser. No. 09/294,851, filed on Apr.20, 1999, now U.S. Pat. No. 6,486,915.

FIELD OF THE INVENTION

This invention is generally related to solid state cameras, and moreparticularly to techniques for determining exposure parameters in suchcameras.

BACKGROUND

Solid state cameras, just like the conventional film camera, are limitedin their ability to take pictures which faithfully replicate the fullrange of colors and brightness in a scene. This is because naturalscenes exhibit a wide dynamic range, i.e., some regions of a scene arevery bright while others are very dark. As a result, conventional solidstate cameras, and particularly consumer products such as digitalcameras and video cameras, have a number of adjustable exposureparameters that control the sensitivity of a camera imager. The bestpictures are usually taken after the camera's exposure parameters havebeen adjusted according to the amount of light in the scene. Forinstance, if the scene is relatively bright, then the exposure, e.g.,the period of time the camera imager is allowed to “sense” the incidentlight, is accordingly reduced so as to better capture the brightnessvariations in the scene. In conventional solid state cameras, a separatelight meter sensor and associated circuitry are used to quickly give animmediate luminance reading of the scene prior to adjusting the exposureparameters and then taking the picture. However, both the light meteringcircuitry and the camera imager must be calibrated, at the time ofmanufacturing the camera, to a reference light source. Otherwise, thetechnique may not yield the proper exposure parameters.

There is a limited conventional technique for determining the optimalexposure that does not use a separate light metering circuit. In thattechnique, the camera is equipped with a means for providing a histogramof the captured scene at a given exposure setting. The histogram shows adistribution of pixel values obtained by the imager at the selectedexposure setting. A person can then manually change the exposure settingand then visually evaluate another histogram of the scene obtained usingthe new exposure setting. The exposure setting is repeatedly adjusted inthis way until the optimal distribution of pixels has been obtained, andthen the picture is taken using this optimal exposure setting. Thistechnique suffers, however, when implemented in commercial solid statecameras, because it is too slow and is not automatic for the averageconsumer who likes the point and shoot convenience of automatic cameras.

SUMMARY

According to an embodiment of the invention, a method is disclosed forautomatically generating a final set of exposure parameters for a solidstate camera having a camera imager, without using a light meteringcircuit separate from the camera imager. An iterative automated searchmethodology is used to arrive at the final set of exposure parametersfrom an initial exposure setting, and sample captures of the scene areevaluated at each trial exposure setting.

In a particular embodiment, the method of generating the final exposuresetting includes selecting one of a number of predetermined exposuresettings as a current exposure setting for the solid state camera. Acaptured scene is then generated by the camera imager using the currentexposure setting. In response to the captured scene being underexposedor overexposed, another one of the exposure settings is selected to bethe current setting according to the automated search methodology. Thetwo latter steps are repeated until the captured scene is neitherunderexposed or overexposed. The search methodology performs a coarsegranularity search so long as the captured scene is either grosslyoverexposed or grossly underexposed, and a fine granularity searchotherwise.

Other features and advantages of the invention will be apparent from theaccompanying drawings and from the detailed description that followsbelow.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is illustrated by way of example and not by way oflimitation in the figures of the accompanying drawings in which likereferences indicate similar elements and in which:

FIG. 1 illustrates a block diagram of an imaging apparatus according toan embodiment of the invention.

FIG. 2 illustrates a flow chart for determining the optimal exposuresetting according to an embodiment of the invention.

FIG. 3 depicts a table of predetermined exposure settings and anapplication of the binary chop search methodology using this table todetermine the optimal setting.

FIG. 4 shows a histogram of pixel values in a captured scene that iscentered out.

FIG. 5 shows a histogram that is centered in.

FIG. 6 illustrates a histogram that may be considered optimal.

FIG. 7 shows a pixel “bucket” with relative amounts of noise and signalbeing identified.

FIG. 8 illustrates exemplary sets of exposure parameters for determiningthe relationship between noise and the exposure parameters of a solidstate camera.

FIG. 9 shows a plot of noise values measured for different integrationtimes, and a best linear fit to such values.

DETAILED DESCRIPTION

An embodiment of the invention is directed to a method for automaticallydetermining an optimal exposure setting prior to taking each pictureusing a solid state camera. The method may be used to determine theproper exposure setting when initiated by a person pressing a camera'sshutter button down at least half-way. The final setting is selectedfrom a number of previously determined exposure parameters using amulti-tiered automated search methodology. These parameters may begeneral and not specific to any particular camera, or they may becustomized for the particular camera. The individual exposure parameterscan be determined by those of ordinary skill in the art by conventionalexposure calculations. Several tests are given here to evaluate theeffectiveness of each selected exposure setting, based on a statisticalanalysis of a sample capture of the scene using that setting. Atechnique is given that computes the expected noise level for variousexposure settings, based on relationships between noise and exposureparameters determined at each power-up of the camera. Use of thisexpected noise level helps achieve speedy and accurate determination ofthe optimal exposure.

In these embodiments, the invention presents a further advantage in thatit reduces manufacturing cost of the camera by eliminating a lightmetering circuit separate from the camera imager. Also, use of thecamera imager for both capturing the final picture and determining theoptimal exposure eliminates calibration steps that would normally berequired to characterize the light metering circuit and the cameraimager at the time of manufacturing the camera. This is in part becausethe sample captured scenes used to determine the exposure setting areobtained through the same imaging data path for the final picture. Thus,the signal level in the final picture will be the same as the signallevel in the sample captured scene and is, therefore, properly accountedfor when determining the optimal exposure setting. An additionaladvantage of certain embodiments of the invention is that they enablethe signal to noise ratio of the final picture to be optimized forambient temperature conditions, so that the full capability of thecamera is realized.

FIG. 1 illustrates an imaging apparatus 100 according to one or moreembodiments of the invention. The apparatus 100 includes optics 104 thathas a conventional aperture, filter, and lens system used to guide theincident light into the camera and onto an imager 108. The imager 108includes a number of photocells 112 normally arranged as a sensor arrayand located at a focal plane of the optics 104. Each photocell 112normally outputs an analog pixel intensity value. These pixel values maythen be subjected to analog processing 116 before being forwarded to ananalog-to-digital (A/D) converter 120. The analog pixel values are thendigitized by the A/D converter 120 and may be subjected to digitalprocessing 124 before being provided as a video data stream, or as stillimages for storage in electronic image file format. These constituentcomponents of the imager 108 may, of course, be implemented in a varietyof different ways. For instance, the photocells 112 and the analogprocessing 116 may be part of the same integrated circuit die. Ifallowed by the die manufacturing process, the A/D converter 120 and thedigital processing 124 may also be integrated onto the same die. Thismay be particularly desirable if the entire imager 108 is implementedusing a complimentary metal oxide semiconductor (CMOS) fabricationprocess. Alternatively, the digital processing 124 may be implementedseparately from the photocells 112 where, for instance, the photocells112 are based on charge coupled device (CCD) technology. In general, theexposure control techniques described here may be implemented using awide range of technologies for the imager 108.

The imager 108 and the optics 104 are under the control of automaticexposure control block 128. The automatic exposure control block 128evaluates digitized pixel values for one or more sample captured scenes,and in response determines the appropriate exposure setting. Eachexposure setting is defined by a number of exposure parameters. Theseinclude aperture size for the optics 104, one or more integration timesapplied to the photocells 112, a gain value (normally an analog gainvalue provided to the analog processing 116), and a flash signal to astrobe 132 used to further illuminate the scene. The exposure settingmay be a combination of one or more of these parameters and perhapsadditional parameters that may need to be controlled to provide the bestquality picture, as recognized by one of ordinary skill in the art.

The integration time defines the amount of time that a photocell 112 isallowed to detect incident light. Depending on the particular technologyused for the photocell, the integration time may be implemented invarious different ways. For instance, in a photocell 112 implemented aspart of a CMOS active pixel sensor, the integration time is the intervalbetween the moment at which a photodiode is isolated, such that itsvoltage is allowed to decay in response to the incident light, and thepoint at which the photodiode voltage is read by external circuitry.

The gain parameter sets the analog voltage and/or current gain to beapplied to the pixel values prior to their being digitized by the A/Dconverter 120. In addition or as an alternative to analog gain, adigital gain applied by the digital processing 124 may be controlled bythe gain parameter.

The aperture parameter controls the amount of incident light that entersthe optics 104. A wide range of different automated aperture mechanismsmay be used to provide the desired range of F-stops. Alternatively, theaperture may be fixed as in certain low-cost consumer cameras.

The analog processing 116 may include correlated double samplingcircuitry, as well as any gain and filtering needed to translate theanalog pixel values into the proper input values required by the A/Dconverter 120. The output range of the A/D converter 120 is typicallyfixed, such as 0-255 for an 8-bit converter, and the entire range ofanalog pixel values are mapped into digitized pixel values in thisrange. The digital processing 124 may be used to format the digitizedpixel values into a form accepted by the automatic exposure controlblock 128. Normally, the exposure control techniques of the invention,and in particular those in which an expected noise value is computed,are applied to raw pixel values, which are not yet subjected to darkcurrent noise reduction or any image processing algorithms. However, itis also possible to use pixel data that has been converted to luminanceor other color filter array interpolated formats.

The automatic exposure control block 128 may be implemented as processorinstructions on a machine-readable medium such as a semiconductormemory, as dedicated hardwired logic for greater speed of execution, oras a combination of the two. In a particular embodiment of theinvention, the imager and the optics form an electronic camera, such asa digital camera, while the exposure control block 128 is implemented bysoftware loaded into a separate data processing device which is notexclusively a stand-alone camera, such as a personal computer.Alternatively, the exposure control block may be integrated in theelectronic camera.

A particular methodology to be followed by the exposure control block128 is shown as a flow chart in FIG. 2. FIG. 2 illustrates an embodimentof the invention that provides an approach to determining the optimalexposure, in conjunction with FIG. 3. FIG. 3 shows a table-based coarseand fine granularity search strategy. In this embodiment, a lookup tablecontains a number of predetermined exposure parameters for each exposuresetting, where each exposure setting may correspond to a givenillumination level. In this example, there are 50 exposure settings thathave been predetermined and are arranged with decreasing exposure asshown. Each exposure setting may have an index value, a gain value, anintegration time, and an aperture size. Operation begins here with step301 in FIG. 2 when the camera is powered up. In step 302, the noise inthe captured raw pixel values is characterized as a function ofintegration time (T_int) and gain in the imaging data path. Techniquesfor determining this noise will be described below. For now, it issufficient to recognize that this noise will be used to set an expectedvalue and an exposure aim value for subsequent captures, prior toanalyzing each capture for underexposure or overexposure.

When the user has aimed the camera at the desired scene and starts todepress the shutter button in step 303, operation continues with step304 in which a sample window of pixels having a camera system-definedposition and size is defined. The sample window will initially encompassall of the scene. Operation then continues with step 308 in which theinitial capture is made with exposure parameters associated with adefault exposure setting/illumination level. The image from thiscaptured sample window is then histogrammed. Operation continuesthereafter with any one of steps 312, 320 or 336. For this example only,operation continues with step 312, in which the test is whether thehistogram data is “centered out.” An example of what is meant bycentered out is shown in FIG. 4, where the pixel values are spreadacross the full range but are “clipped” at the minimum (noise floor) ormaximum (2^(N), where ^(N) is the number of bits provided by the A/Doutput). Clipping occurs when, for instance, 5% of the total number ofpixels in the sample window have the maximum or minimum value. Test 312will also fail if the sample window is already at the minimum sizeallowed.

If the test in step 312 is true, then this means that the imager'sdynamic range is far too small to capture the whole scene's dynamicrange. Thus, the current sample window may not be the best window todetermine the optimal exposure setting for this particular scene. Inthis case, operation will proceed with step 316 in which the samplewindow is reduced to concentrate effort on determining a final exposurefor the main subject, which is likely positioned in the center of thescene. This change may be beneficial in that it might exclude certainperipheral elements in the scene which may be less important to renderwith detail, such as, for instance, the sun in the sky. For instance,making the sample window smaller and centered over the captured scene isgenerally appropriate for most consumer applications, as consumers tendto center the subject of interest when taking a picture. The reductionin window size is allowed until the size reaches a predeterminedminimum. After changing the window, operation then loops back to step308 where a histogram is performed of the new sample window and the testin step 312 is repeated.

If the test in step 312 is not true, then the next test may be step 320to determine whether the histogram data is “centered in,” as shown inFIG. 5. The term centered in may loosely describe a histogram in whichthe pixels are spread across a significant portion of the full A/Dconverter range and exhibit no significant clipping, if any, at eitherextreme. If the histogram is not centered in, then the next exposuresetting will be selected based on an efficient table search strategywith coarse granularity (see FIG. 3). For example, the binary chop isknown to be a highly efficient coarse granularity search technique. Inthat case, operation proceeds with step 324 in which the captured scene,and, in this particular embodiment, data from just the sample window, isevaluated to determine whether the current exposure setting yielded acapture that is underexposed, i.e., too dark. An underexposed scenemeans that the histogram will show few, if any, mid or light tones. Thismay occur, for instance, if the maximum value in the histogram is lessthan an aim mean value for the histogram. If so, then the next exposureshould be greater, i.e., longer integration time, increased gain and/oraperture size. If underexposed, then operation proceeds with step 328 inwhich the search algorithm is applied to select a greater exposuresetting.

Returning to step 324, if the capture has few, if any, mid or darktones, such as when the minimum histogram value is greater than the aimmean, then the image is overexposed. In a particular embodiment of theinvention, this aim mean is 18% of the maximum digitized signal range,i.e., noise value +(2^(N)−1−noise value) * 0.18. This is based on theassumption that the optimal exposure setting for the scene is the sameas that needed to properly expose an equivalent 18% gray card, under thesame lighting as the scene being captured. If overexposed, thenoperation proceeds with step 332, such as by performing a binary chop ofthe current index range (between top and bottom) as in table 404 a, andthen resetting the range, as shown in table 404 b (FIG. 3).

Returning now to decision step 320, if the centered in test is true,then the current exposure setting resulted in a capture that is neithergrossly overexposed or grossly underexposed. In addition, if preceded bytest 312 for “centered out,” this will also mean that any clipping atthe outer limits of the histogram is either not an issue or has beenaddressed to the best of the system's ability. As a result, the searchmethodology for the next exposure setting changes from coarsegranularity to fine granularity, beginning with step 336.

In step 336, the histogram is further tested to determine whether thehistogram mean is within the allowable tolerance of the aim mean. Ifthis test is true, then the optimal exposure setting has been found,such that the final picture may be taken using this setting. If,however, the test in step 336 is not true, then operation continues withstep 340 in which the mean of the current histogram and the mean of ahistogram of a previous capture are compared to the aim mean. If the aimmean is straddled by the mean values of the current and previouscaptures, then operation continues with step 344 in which the exposuresetting that yielded the histogram mean being closest to the aim mean ischosen to be the optimal setting. The straddling of the aim mean mayoccur if the captured scene, and in particular the sample window, hasexceedingly high contrast. The tolerance around an 18% aim mean valuemay be selected by one of ordinary skill in the art following an errorbudget analysis to determine the best exposure tolerance bands. Fordigital cameras having imagers built using metal oxide semiconductor(MOS) fabrication processes and 8 bit digitized pixel values, an aimingfor less than ¼ exposure value (EV) error and given a black level (noisefloor) of 54 A/D units, an aim mean of 90 units may be selected with atolerance of +/−6 A/D units.

If the outcome of the straddle test in step 340 is false, then thehistogram mean using the current exposure setting is compared to the aimmean of the histogram. If the histogram mean is greater than the aimmean, i.e., overexposed, then the exposure setting index is incrementedto the next adjacent (higher) inferred illumination level and itscorresponding exposure setting. If the histogram mean is less than theaim mean, i.e., underexposed, then the index is decremented to the nextadjacent (lower) inferred illumination level and its correspondingexposure setting (see FIG. 3, table 404 c). Operation then loops back tostep 308 where another capture of the scene is made using the newexposure setting.

FIG. 3 shows the use of coarse and fine granularity searches in a listof exposure settings to determine the optimal exposure. In thisembodiment, a lookup table is created that contains a number of exposureparameters for each exposure setting. In this example, there are 50exposure settings that have been predetermined and are arranged indecreasing exposure as shown. Each setting may be defined by a gainvalue, an integration time, and an aperture size. Other exposureattributes, such as flash use, may also be included. Operation willbegin with selecting a current exposure setting at the index position10. The camera will then obtain the gain, integration time and aperturesize associated with index 10 in the lookup table. A capture of thescene using this current exposure setting will then be executed andevaluated. Rather than selecting the initial exposure setting as thehalfway point between the top and bottom of the table, the initialsetting is selected slightly closer to the top, because the integrationtimes in the upper half of the table are longer than those in the lowerhalf of the table, so that a completed search using an initial exposuresetting from near the top of the table will consume a more uniformamount of time for any possible illumination level.

Thus, with the current setting at index 10 yielding an overexposedscene, a binary chop is performed to select the next setting at index30, which is halfway between a current setting and the bottom of thetable. The scene is then captured again using the new current setting atindex 30. Note that the top boundary of the range of exposure settingsin Table 404(b) is now at index 10. If the current exposure setting atindex 30 results in an underexposed scene, then the binary chop willpick the point halfway between index 10 and index 30, which is index 20.In Table 404(c), the current setting is now at index 20. Assume now thatthe current setting at index 20 results in a capture of the scene havinga centered in characteristic, as determined by its histogram. This meansthat the captured scene is only mildly over—or underexposed, such thatthe binary chop algorithm should be abandoned in favor of an incrementalstep to the next adjacent exposure setting. Thus, if the histogram dataindicates that the current captured scene is still underexposed, thenthe exposure setting index is decremented from 20 to 19, as shown by thepointers in Table 404(d). Once again, if the captured scene using thecurrent setting at index 19 is still underexposed, then the settingindex is decremented to 18. Finally, if the histogram mean of thecaptured scene obtained using the current setting at index 18 is withinthe tolerances surrounding the aim mean, then the optimal exposuresetting is found, as indicated in Table 404(e).

The inventors have discovered that it is useful to switch from coarsegranularity to fine granularity, while searching for the final exposureamong a number of predetermined exposure settings, when the currentcapture of the scene becomes under—or overexposed at a relatively mildlevel. Otherwise, continued use of coarse granularity for selecting thenext exposure setting may not converge to the final setting. The failureof a course granularity search such as the binary chop may occur becausethe mean of the pixel values in each capture shifts with differentexposure levels. When the scene dynamic range exceeds what a camera maycapture, pixels will be clipped at the maximum and minimum A/Dboundaries. As different exposures change the amount of incoming light,some clipped pixels at one A/D extreme will no longer be clipped. This,in turn, affects the histogram's mean value which is an input parameterof the binary chop technique. Because this input parameter changes in aninexact, unpredictable manner with different exposures, the binary chopwill fail to converge for some scenes.

The embodiments of the invention described above utilize the concept ofan aim mean that is compared to a histogram mean to help determine whenthe search methodology should switch from coarse granularity to finegranularity and also when the captured scene is over or underexposed. Ina particular embodiment of the invention, the aim mean is replaced witha “dynamic” aim mean that is computed as a function of each exposuresetting. A dynamic aim mean as defined here is a noise-dependentvariable that is computed for each captured scene, based on the currentexposure setting. The possible sources of noise that may be taken intoaccount when determining the dynamic aim mean are illustrated in FIG. 7.This figure shows a pixel “bucket” showing the relative amounts of thedifferent types of noise that are captured in a conventional digitalcamera. These types contribute to the aforementioned noise floor andshould be considered when determining a formula for the noise floor. Ina particular embodiment of the invention, the dynamic aim mean isdefined as follows:Dynamic Aim Mean Δ (18%)(2^(N)−1−Dynamic Mean Noise)+Dynamic Mean Noisewhere the Dynamic Mean Noise is defined as the expected mean of thenoise floor (a function of integration time and gain, see below). In theexample given in this disclosure, N=8 bits.

The inventors have determined that the final exposure setting for takinga picture using a digital camera may be found relatively quickly usingthe above-described automated methodology when the histogram mean iscompared to a dynamic aim mean computed for each given exposure setting.In a further embodiment of the invention that uses the dynamic aim mean,mathematical relationships that define a noise variable as a function ofdifferent exposure parameter variables are determined. Theserelationships may be linear (y=ax+b, where y is noise and x is theexposure parameter) or may alternatively be of higher order if needed tomore accurately describe the relationship. They can be determined byfitting a curve or line to the measured test data, as shown in theexample of FIGS. 8 and 9. FIG. 8 shows a set of exposures that will becaptured with the shutter in the closed condition to assess the camera'snoise level at the current temperature. Note that the series of exposureparameters in FIG. 8 have an integration time series at a fixed gain anda gain series at fixed integration time. The time series is used toderive a general relationship for noise vs. integration time. This canbe done linearly, as illustrated in FIG. 9, or as a higher orderregression as needed. The test data may be gathered from a set of closedshutter captures (dark frames) obtained at the ambient temperature atwhich the final picture is to be taken. The closed shutter captures canbe obtained upon camera power-up as illustrated in FIG. 2 step 302 or atany other convenient time, normally prior to the user depressing theshutter button to take a picture. By using such predefined mathematicalrelationships between the noise floor and the various exposureparameters, there is no need to capture and process any dark frames eachtime a picture of a new, different scene is taken, thus promoting aspeedier determination of the final exposure. The mathematicalrelationships for the present embodiments include noise vs. pixelintegration time, N(Tint), and noise vs. gain in the imaging data pathprior to digitization, N(G). For instance, if the N(Tint) line isdescribed by a linear fit, N(Tint)=a₃* T+b₁, and if the N(G) line isdescribed by N(G)=a₅* G+b₂, then the dynamic mean noise can be given by:dynamic mean noise∝a ₃ *T_integration+a ₅*gain+b ₅where b₅=b₁+b₂ and where proportionality constants have been omitted. Byusing such predetermined mathematical formulas to determine the noisefloor, the dynamic mean noise, and the dynamic aim mean as a function ofeach trial exposure setting, a more accurate determination of theexposure setting may be obtained. Use of such a technique also allowsthe dynamic range of the scene to be mapped onto the camera's availabledynamic range.

To summarize, various embodiments of the invention as a method fordetermining a final exposure setting automatically for a solid statecamera without a separate light metering circuit have been described. Inthe foregoing specification, the invention has been described withreference to specific exemplary embodiments thereof. It will, however,be evident that various modifications and changes may be made theretowithout departing from the broader spirit and scope of the invention asset forth in the appended claims. For instance, the exposuredetermination techniques described above may be applied to a wide rangeof solid state cameras, including video cameras. Also, the invention isnot limited to the centered in and centered out tests described above.One of ordinary skill in the art after having read this disclosure maybe able to develop alternative tests for determining when a series ofcaptures change from being grossly overexposed or grossly underexposedto being only mildly so. The specification and drawings are,accordingly, to be regarded in an illustrative rather that a restrictivesense.

1. A method for generating an exposure setting, comprising: a) selectingone of a plurality of predetermined exposure settings as a currentexposure setting for a solid state camera having a camera imager; b)generating a captured scene by the camera imager using the currentexposure setting; c) selecting according to an automated searchmethodology another one of the exposure settings to be the currentsetting in response to determining the captured scene as beingunderexposed or overexposed; and d) repeating b) and c) until thecaptured scene is neither underexposed or overexposed, wherein thesearch methodology performs a coarse granularity search so long as thecaptured scene is either grossly overexposed or grossly underexposed,and then changes to a fine granularity search if the captured scene isstill overexposed or underexposed but not grossly so, wherein the searchmethodology performs the coarse granularity search, when selectinganother one of the exposure settings, so long as one of the following istrue: (1) there are few pixels in the captured scene, using the currentexposure setting, that are above a noise threshold, and (2) there aremany pixels in the captured scene that are above a maximum threshold;and if not then the search methodology performs the fine granularitysearch.
 2. The method of claim 1 wherein the determination of whetherthe captured scene is grossly under or overexposed is made by comparingcaptured pixels, using the current exposure setting, to a function thatis defined to be of a noise variable representing noise in an imagingdata path of the camera imager.
 3. The method of claim 2 wherein thenoise variable is defined as a noise function of one or more exposureparameters, the function being determined by analyzing pixel values in aplurality of dark frame portions captured by the solid state cameraunder different exposure settings.
 4. The method of claim 3 wherein thenoise function is determined upon camera power-up, before the camera isready to take pictures, when the plurality of dark frame portions arecaptured and histogrammed, an arithmetic mean is computed for eachhistogram, and an equation that relates the noise variable to saidexposure parameter variables is fitted to data points derived from thearithmetic mean computation.
 5. The method of claim 1 wherein theplurality of predetermined exposure settings are arranged in a monotonicsequence.
 6. The method of claim 1 wherein each exposure setting is inpart defined by a set of exposure parameters for the camera, theexposure parameters including a gain applied in the imaging data path.7. The method of claim 6 wherein the gain is applied to analog pixelsignals prior to their being digitized.
 8. A method for generating anexposure setting, comprising: a) selecting one of a plurality ofpredetermined exposure settings as a current exposure setting for asolid state camera having a camera imager; b) generating a capturedscene by the camera imager using the current exposure setting; c)selecting another one of the exposure settings to be the current settingin response to determining the captured scene as being underexposed oroverexposed; and d) repeating b) and c) in response to the capturedscene being determined to be underexposed or overexposed, wherein theselecting is in accordance with a coarse granularity search so long asthe captured scene is either grossly overexposed or grosslyunderexposed, and then changes to a fine granularity search if thecaptured scene is still overexposed or underexposed but not grossly so,wherein the coarse granularity search is performed, when selectinganother one of the exposure settings and is sequentially repeated indetermining whether to transition to the fine granularity search, solong as one of the following is true: (1) there are few pixels in thecaptured scene, using the current exposure setting, that are above anoise threshold, and (2) there are many pixels in the captured scenethat are above a maximum threshold; and if not, the fine granularitysearch is performed.
 9. The method of claim 8 wherein the determinationof whether the captured scene is grossly under or overexposed is made bycomparing captured pixels, using the current exposure setting, to afunction that is defined to be of a noise variable representing noise inan imaging data path of the camera imager.
 10. The method of claim 9wherein the noise variable is defined as a noise function of one or moreexposure parameters, the function being determined by analyzing pixelvalues in a plurality of dark frame portions captured by the solid statecamera under different exposure settings.
 11. The method of claim 10wherein the noise function is determined upon camera power-up, beforethe camera is ready to take pictures, when the plurality of dark frameportions are captured and histogrammed, an arithmetic mean is computedfor each histogram, and an equation that relates the noise variable tosaid exposure parameter variables is fitted to data points derived fromthe arithmetic mean computation.
 12. The method of claim 8 wherein eachexposure setting is in part defined by a set of exposure parameters forthe camera, the exposure parameters including a gain applied in theimaging data path.